Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm.

The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping

Particle Image Velocimetry Data Processing On A Gpu Cluster

Paper presented at 2018 Canadian Society of Mechanical Engineers International Congress, 27-30 May 2018.Particle image velocimetry (PIV) data processing is a computationally expensive process. The immense time taken to analyze data can limit the maximum dataset size. Using graphics processing units (GPUs) has been shown to drastically decrease the processing time for PIV image pairs. The open-source PIV data processing software OpenPIV has been ported to run on a GPU to boost speed and efficiency and has outperformed the CPU version of the software. A multipass method is being implemented in OpenPIV to improve both speed and accuracy. The completed algorithm will be tested on an embedder CPU-GPU device, a desktop computer, and the SOSCIP GPU-accelerated supercomputing cluster. Ultimately, OpenPIV will run on a wide variety of computer platforms an enable larger datasets to be collects, leading to better statistics on the resulting velocity fields

Asymptotic dimension and small-cancellation for hierarchically hyperbolic spaces and groups

We prove that all hierarchically hyperbolic spaces have finite asymptotic dimension and obtain strong bounds on these dimensions. One application of this result is to obtain the sharpest known bound on the asymptotic dimension of the mapping class group of a finite type surface: improving the bound from exponential to at most quadratic in the complexity of the surface. We also apply the main result to various other hierarchically hyperbolic groups and spaces. We also prove a small-cancellation result namely: if $G$ is a hierarchically hyperbolic group, $H\leq G$ is a suitable hyperbolically embedded subgroup, and $N\triangleleft H$ is "sufficiently deep" in $H$, then $G/\langle\langle N\rangle\rangle$ is a relatively hierarchically hyperbolic group. This new class provides many new examples to which our asymptotic dimension bounds apply. Along the way, we prove new results about the structure of HHSs, for example: the associated hyperbolic spaces are always obtained, up to quasi-isometry, by coning off canonical coarse product regions in the original space (generalizing a relation established by Masur--Minsky between the complex of curves of a surface and Teichm\"{u}ller space).Comment: Minor revisions in Section 6. This is the version accepted for publicatio

Fast kk-NNG construction with GPU-based quick multi-select

In this paper we describe a new brute force algorithm for building the $k$-Nearest Neighbor Graph ($k$-NNG). The $k$-NNG algorithm has many applications in areas such as machine learning, bio-informatics, and clustering analysis. While there are very efficient algorithms for data of low dimensions, for high dimensional data the brute force search is the best algorithm. There are two main parts to the algorithm: the first part is finding the distances between the input vectors which may be formulated as a matrix multiplication problem. The second is the selection of the $k$-NNs for each of the query vectors. For the second part, we describe a novel graphics processing unit (GPU) -based multi-select algorithm based on quick sort. Our optimization makes clever use of warp voting functions available on the latest GPUs along with use-controlled cache. Benchmarks show significant improvement over state-of-the-art implementations of the $k$-NN search on GPUs

Real-Time Riverine Particle Image Velocimetry

A modular particle image velocimetry program was developed and optimized to read and process video of river surface flows from different sensor types. The program was designed for long-term deployment with the ability to sample data continuously in realtime and save the results in a compact format. The time needed to compute a velocity measurement from video input was reduced by using concurrent processing techniques, multi-threading, and a graphics hardware-based correlation algorithm. When used to process field data on a low power Intel Atom based computer the PIV system was capable of computing up to 64 velocity measurements at a rate of 7.5 frames per second. A more powerful computer equipped with a discrete GPU was capable of computing 4800 velocity measurements at a rate of 7.5 frames per second when using the same PIV data and settings. Processing speed of the GPU correlation module was analyzed using a number of different benchmarks. Results show that the GPU-based correlation algorithm has the potential to improve the PIV processing speed of high-end workstations by as much as 2x and low-end portable computers by 10-20x. Methods were also introduced to improve the quality of PIV measurements on river currents. Processing video of river currents with the standard particle image velocimetry technique produced a large number of inaccurate vectors. Most of these inaccurate vectors were correctly identified and removed by using different confidence scoring and filtering techniques. Results from three different experiments were compared to the velocity measurements of other devices to verify the accuracy of the program. These measurements agree to within 16% difference. These results show that accurate PIV measurements of river surface velocity may be computed in real time even on low end and portable computer hardware

Best Practices for Volume Flow Rate Measurements Using PIV at the Exit of a Turbulent Planar Jet

Particle image velocimetry (PIV) is used to make volume-flow-rate measurements at the exit of a turbulent, planar nozzle. The objective of this report is to assess a range of data acquisition and processing parameters. Data is acquired for volume flow rates of Reynolds numbers between 10,000 and 100,000 for both two-component (2C) and stereo PIV. The parameters are systematically changed one at a time and evaluated using differences in uncertainty, calculation time, and volume- flow-rate deviation. Data acquisition parameters follow the trends of previous work. A multitude of processing parameters were varied for several PIV processing methods. Recommendations for each method are developed and listed with potential drawbacks. 2C PIV was found to underestimate volume-flow-rate by 3-4% depending on the integration scheme and stereo PIV underestimated volume-flow-rate by 2%